In this report, we extract information about published JOSS papers and generate graphics as well as a summary table that can be downloaded and used for further analyses.
suppressPackageStartupMessages({
library(tibble)
library(rcrossref)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
library(gh)
library(purrr)
library(jsonlite)
library(DT)
library(plotly)
library(citecorp)
library(readr)
})## Keep track of the source of each column
source_track <- c()
## Determine whether to add a caption with today's date to the (non-interactive) plots
add_date_caption <- TRUE
if (add_date_caption) {
dcap <- lubridate::today()
} else {
dcap <- ""
}## Read archived version of summary data frame, to use for filling in
## information about software repositories (due to limit on API requests)
## Sort by the date when software repo info was last obtained
papers_archive <- readRDS(gzcon(url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_analytics.rds?raw=true"))) %>%
dplyr::arrange(!is.na(repo_info_obtained), repo_info_obtained)
## Similarly for citation analysis, to avoid having to pull down the
## same information multiple times
citations_archive <- readr::read_delim(
url("https://github.com/openjournals/joss-analytics/blob/gh-pages/joss_submission_citations.tsv?raw=true"),
col_types = cols(.default = "c"), col_names = TRUE,
delim = "\t")We get the information about published JOSS papers from Crossref, using the rcrossref R package. This package is also used to extract citation counts.
## Fetch JOSS papers from Crossref
## Only 1000 papers at the time can be pulled down
lim <- 1000
papers <- rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim)$data
i <- 1
while (nrow(papers) == i * lim) {
papers <- dplyr::bind_rows(
papers,
rcrossref::cr_works(filter = c(issn = "2475-9066"),
limit = lim, offset = i * lim)$data)
i <- i + 1
}
papers <- papers %>%
dplyr::filter(type == "journal-article")
## A few papers don't have DOIs - generate them from the URL
noaltid <- which(is.na(papers$alternative.id))
papers$alternative.id[noaltid] <- gsub("http://dx.doi.org/", "",
papers$url[noaltid])
## Get citation info from Crossref and merge with paper details
cit <- rcrossref::cr_citation_count(doi = papers$alternative.id)
papers <- papers %>% dplyr::left_join(
cit %>% dplyr::rename(citation_count = count),
by = c("alternative.id" = "doi")
)
## Remove one duplicated paper
papers <- papers %>% dplyr::filter(alternative.id != "10.21105/joss.00688")
source_track <- c(source_track,
structure(rep("crossref", ncol(papers)),
names = colnames(papers)))For each published paper, we use the Whedon API to get information about pre-review and review issue numbers, corresponding software repository etc.
whedon <- list()
p <- 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
while (length(a) > 0) {
whedon <- c(whedon, a)
p <- p + 1
a <- jsonlite::fromJSON(
url(paste0("https://joss.theoj.org/papers/published.json?page=", p)),
simplifyDataFrame = FALSE
)
}
whedon <- do.call(dplyr::bind_rows, lapply(whedon, function(w) {
data.frame(api_title = w$title,
api_state = w$state,
editor = paste(w$metadata$paper$editor, collapse = ","),
reviewers = paste(w$reviewers, collapse = ","),
nbr_reviewers = length(w$reviewers),
repo_url = w$repository_url,
review_issue_id = w$review_issue_id,
doi = w$doi,
prereview_issue_id = ifelse(!is.null(w$meta_review_issue_id),
w$meta_review_issue_id, NA_integer_),
languages = paste(w$metadata$paper$languages, collapse = ","),
archive_doi = w$metadata$paper$archive_doi)
}))
papers <- papers %>% dplyr::left_join(whedon, by = c("alternative.id" = "doi"))
source_track <- c(source_track,
structure(rep("whedon", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))From each pre-review and review issue, we extract information about review times and assigned labels.
## Pull down info on all issues in the joss-reviews repository
issues <- gh("/repos/openjournals/joss-reviews/issues",
.limit = 5000, state = "all")## From each issue, extract required information
iss <- do.call(dplyr::bind_rows, lapply(issues, function(i) {
data.frame(title = i$title,
number = i$number,
state = i$state,
opened = i$created_at,
closed = ifelse(!is.null(i$closed_at),
i$closed_at, NA_character_),
ncomments = i$comments,
labels = paste(setdiff(
vapply(i$labels, getElement,
name = "name", character(1L)),
c("review", "pre-review", "query-scope", "paused")),
collapse = ","))
}))
## Split into REVIEW, PRE-REVIEW, and other issues (the latter category
## is discarded)
issother <- iss %>% dplyr::filter(!grepl("\\[PRE REVIEW\\]", title) &
!grepl("\\[REVIEW\\]", title))
dim(issother)## [1] 109 7
## title
## 1 I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.
## 2 Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
## 3 State of the field: Do the authors describe how this software compares to other commonly-used packages?
## 4 Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
## 5 A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
## 6 A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
## number state opened closed ncomments labels
## 1 4016 closed 2021-12-20T16:33:44Z 2021-12-20T16:33:47Z 1
## 2 3995 closed 2021-12-14T20:28:26Z 2021-12-14T20:28:29Z 2
## 3 3989 closed 2021-12-10T18:07:16Z 2021-12-10T18:07:21Z 2
## 4 3966 closed 2021-12-01T11:29:56Z 2021-12-01T11:30:01Z 1
## 5 3952 closed 2021-11-24T15:25:26Z 2021-11-24T15:25:30Z 1
## 6 3939 closed 2021-11-21T19:17:42Z 2021-11-21T19:17:47Z 1
## For REVIEW issues, generate the DOI of the paper from the issue number
getnbrzeros <- function(s) {
paste(rep(0, 5 - nchar(s)), collapse = "")
}
issrev <- iss %>% dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::mutate(nbrzeros = purrr::map_chr(number, getnbrzeros)) %>%
dplyr::mutate(alternative.id = paste0("10.21105/joss.",
nbrzeros,
number)) %>%
dplyr::select(-nbrzeros) %>%
dplyr::mutate(title = gsub("\\[REVIEW\\]: ", "", title)) %>%
dplyr::rename_at(vars(-alternative.id), ~ paste0("review_", .))## For pre-review and review issues, respectively, get the number of
## issues closed each month, and the number of those that have the
## 'rejected' label
review_rejected <- iss %>%
dplyr::filter(grepl("\\[REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "review")
prereview_rejected <- iss %>%
dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!is.na(closed)) %>%
dplyr::mutate(closedmonth = lubridate::floor_date(as.Date(closed), "month")) %>%
dplyr::group_by(closedmonth) %>%
dplyr::summarize(nbr_issues_closed = length(labels),
nbr_rejections = sum(grepl("rejected", labels))) %>%
dplyr::mutate(itype = "pre-review")
all_rejected <- dplyr::bind_rows(review_rejected, prereview_rejected)## For PRE-REVIEW issues, add information about the corresponding REVIEW
## issue number
isspre <- iss %>% dplyr::filter(grepl("\\[PRE REVIEW\\]", title)) %>%
dplyr::filter(!grepl("withdrawn", labels)) %>%
dplyr::filter(!grepl("rejected", labels))
## Some titles have multiple pre-review issues. In these cases, keep the latest
isspre <- isspre %>% dplyr::arrange(desc(number)) %>%
dplyr::filter(!duplicated(title)) %>%
dplyr::mutate(title = gsub("\\[PRE REVIEW\\]: ", "", title)) %>%
dplyr::rename_all(~ paste0("prerev_", .))
papers <- papers %>% dplyr::left_join(issrev, by = "alternative.id") %>%
dplyr::left_join(isspre, by = c("prereview_issue_id" = "prerev_number")) %>%
dplyr::mutate(prerev_opened = as.Date(prerev_opened),
prerev_closed = as.Date(prerev_closed),
review_opened = as.Date(review_opened),
review_closed = as.Date(review_closed)) %>%
dplyr::mutate(days_in_pre = prerev_closed - prerev_opened,
days_in_rev = review_closed - review_opened,
to_review = !is.na(review_opened))
source_track <- c(source_track,
structure(rep("joss-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))## Reorder so that software repositories that were interrogated longest
## ago are checked first
tmporder <- order(match(papers$alternative.id, papers_archive$alternative.id),
na.last = FALSE)
software_urls <- papers$repo_url[tmporder]
is_github <- grepl("github", software_urls)
length(is_github)## [1] 1496
## [1] 1423
## [1] "https://gitlab.com/fduchate/predihood"
## [2] "https://gitlab.com/mmartin-lagarde/exonoodle-exoplanets/-/tree/master/"
## [3] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/utopia"
## [4] "https://bitbucket.org/meg/cbcbeat"
## [5] "https://ts-gitlab.iup.uni-heidelberg.de/dorie/dorie"
## [6] "https://gitlab.com/myqueue/myqueue"
## [7] "https://gitlab.inria.fr/bramas/tbfmm"
## [8] "https://bitbucket.org/hammurabicode/hamx"
## [9] "https://gitlab.inria.fr/miet/miet"
## [10] "https://gitlab.com/cerfacs/batman"
## [11] "https://ts-gitlab.iup.uni-heidelberg.de/utopia/dantro"
## [12] "https://gitlab.com/emd-dev/emd"
## [13] "http://mutabit.com/repos.fossil/grafoscopio/"
## [14] "https://gitlab.com/libreumg/dataquier.git"
## [15] "https://bitbucket.org/cardosan/brightway2-temporalis"
## [16] "https://savannah.nongnu.org/projects/complot/"
## [17] "https://gitlab.com/manchester_qbi/manchester_qbi_public/madym_cxx/"
## [18] "https://bitbucket.org/manuela_s/hcp/"
## [19] "https://gitlab.com/gdetor/genetic_alg"
## [20] "https://gitlab.com/jason-rumengan/pyarma"
## [21] "https://gitlab.com/ffaucher/hawen"
## [22] "https://gricad-gitlab.univ-grenoble-alpes.fr/ttk/spam/"
## [23] "https://gitlab.com/dlr-dw/ontocode"
## [24] "https://bitbucket.org/rram/dvrlib/src/joss/"
## [25] "https://gitlab.gwdg.de/mpievolbio-it/crbhits"
## [26] "https://gitlab.com/project-dare/dare-platform"
## [27] "https://bitbucket.org/clhaley/Multitaper.jl"
## [28] "https://git.rwth-aachen.de/ants/sensorlab/imea"
## [29] "https://gitlab.com/remram44/taguette"
## [30] "https://gitlab.ethz.ch/holukas/dyco-dynamic-lag-compensation"
## [31] "https://gitlab.com/vibes-developers/vibes"
## [32] "https://gitlab.com/marinvaders/marinvaders"
## [33] "https://earth.bsc.es/gitlab/wuruchi/autosubmitreact"
## [34] "https://gitlab.com/sails-dev/sails"
## [35] "https://bitbucket.org/glotzer/rowan"
## [36] "https://bitbucket.org/sciencecapsule/sciencecapsule"
## [37] "https://gitlab.com/QComms/cqptoolkit"
## [38] "https://bitbucket.org/cdegroot/wediff"
## [39] "https://gitlab.com/toposens/public/ros-packages"
## [40] "https://gitlab.com/eidheim/Simple-Web-Server"
## [41] "https://framagit.org/GustaveCoste/eldam"
## [42] "https://www.idpoisson.fr/fullswof/"
## [43] "https://bitbucket.org/basicsums/basicsums"
## [44] "https://gitlab.inria.fr/azais/treex"
## [45] "https://bitbucket.org/mpi4py/mpi4py-fft"
## [46] "https://gitlab.inria.fr/mosaic/bvpy"
## [47] "https://git.iws.uni-stuttgart.de/tools/frackit"
## [48] "https://bitbucket.org/likask/mofem-cephas"
## [49] "https://bitbucket.org/miketuri/perl-spice-sim-seus/"
## [50] "https://bitbucket.org/ocellarisproject/ocellaris"
## [51] "https://bitbucket.org/dolfin-adjoint/pyadjoint"
## [52] "https://gitlab.com/davidtourigny/dynamic-fba"
## [53] "https://gitlab.com/LMSAL_HUB/aia_hub/aiapy"
## [54] "https://gitlab.com/materials-modeling/wulffpack"
## [55] "https://bitbucket.org/cmutel/brightway2"
## [56] "https://bitbucket.org/berkeleylab/esdr-pygdh/"
## [57] "https://gitlab.com/cosmograil/PyCS3"
## [58] "https://sourceforge.net/p/mcapl/mcapl_code/ci/master/tree/"
## [59] "https://gitlab.com/dlr-ve/autumn/"
## [60] "https://gitlab.com/moorepants/skijumpdesign"
## [61] "https://gitlab.com/gims-developers/gims"
## [62] "https://gitlab.com/geekysquirrel/bigx"
## [63] "https://bitbucket.org/cloopsy/android/"
## [64] "https://bitbucket.org/dghoshal/frieda"
## [65] "https://gitlab.com/celliern/scikit-fdiff/"
## [66] "https://gitlab.com/costrouc/pysrim"
## [67] "https://gitlab.com/ampere2/metalwalls"
## [68] "https://gitlab.com/energyincities/besos/"
## [69] "https://gitlab.com/tesch1/cppduals"
## [70] "https://doi.org/10.17605/OSF.IO/3DS6A"
## [71] "https://c4science.ch/source/tamaas/"
## [72] "https://bitbucket.org/mituq/muq2.git"
## [73] "https://gitlab.com/datafold-dev/datafold/"
df <- do.call(dplyr::bind_rows, lapply(software_urls[is_github], function(u) {
u0 <- gsub("^http://", "https://", gsub("\\.git$", "", gsub("/$", "", u)))
if (grepl("/tree/", u0)) {
u0 <- strsplit(u0, "/tree/")[[1]][1]
}
if (grepl("/blob/", u0)) {
u0 <- strsplit(u0, "/blob/")[[1]][1]
}
info <- try({
gh(gsub("(https://)?(www.)?github.com/", "/repos/", u0))
})
languages <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/languages"),
.limit = 500)
})
topics <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/topics"),
.accept = "application/vnd.github.mercy-preview+json", .limit = 500)
})
contribs <- try({
gh(paste0(gsub("(https://)?(www.)?github.com/", "/repos/", u0), "/contributors"),
.limit = 500)
})
if (!is(info, "try-error") && length(info) > 1) {
if (!is(contribs, "try-error")) {
if (length(contribs) == 0) {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
} else {
repo_nbr_contribs <- length(contribs)
repo_nbr_contribs_2ormore <- sum(vapply(contribs, function(x) x$contributions >= 2, NA_integer_))
if (is.na(repo_nbr_contribs_2ormore)) {
print(contribs)
}
}
} else {
repo_nbr_contribs <- repo_nbr_contribs_2ormore <- NA_integer_
}
if (!is(languages, "try-error")) {
if (length(languages) == 0) {
repolang <- ""
} else {
repolang <- paste(paste(names(unlist(languages)),
unlist(languages), sep = ":"), collapse = ",")
}
} else {
repolang <- ""
}
if (!is(topics, "try-error")) {
if (length(topics$names) == 0) {
repotopics <- ""
} else {
repotopics <- paste(unlist(topics$names), collapse = ",")
}
} else {
repotopics <- ""
}
data.frame(repo_url = u,
repo_created = info$created_at,
repo_updated = info$updated_at,
repo_pushed = info$pushed_at,
repo_nbr_stars = info$stargazers_count,
repo_language = ifelse(!is.null(info$language),
info$language, NA_character_),
repo_languages_bytes = repolang,
repo_topics = repotopics,
repo_license = ifelse(!is.null(info$license),
info$license$key, NA_character_),
repo_nbr_contribs = repo_nbr_contribs,
repo_nbr_contribs_2ormore = repo_nbr_contribs_2ormore
)
} else {
NULL
}
})) %>%
dplyr::mutate(repo_created = as.Date(repo_created),
repo_updated = as.Date(repo_updated),
repo_pushed = as.Date(repo_pushed)) %>%
dplyr::distinct() %>%
dplyr::mutate(repo_info_obtained = lubridate::today())
stopifnot(length(unique(df$repo_url)) == length(df$repo_url))
dim(df)
## For papers not in df (i.e., for which we didn't get a valid response
## from the GitHub API query), use information from the archived data frame
dfarchive <- papers_archive %>%
dplyr::select(colnames(df)[colnames(df) %in% colnames(papers_archive)]) %>%
dplyr::filter(!(repo_url %in% df$repo_url))
df <- dplyr::bind_rows(df, dfarchive)
papers <- papers %>% dplyr::left_join(df, by = "repo_url")
source_track <- c(source_track,
structure(rep("sw-github", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))## Convert publication date to Date format
## Add information about the half year (H1, H2) of publication
## Count number of authors
papers <- papers %>% dplyr::select(-reference, -license, -link) %>%
dplyr::mutate(published.date = as.Date(published.print)) %>%
dplyr::mutate(
halfyear = paste0(year(published.date),
ifelse(month(published.date) <= 6, "H1", "H2"))
) %>% dplyr::mutate(
halfyear = factor(halfyear,
levels = paste0(rep(sort(unique(year(published.date))),
each = 2), c("H1", "H2")))
) %>% dplyr::mutate(nbr_authors = vapply(author, function(a) nrow(a), NA_integer_))
papers <- papers %>% dplyr::distinct()
source_track <- c(source_track,
structure(rep("cleanup", length(setdiff(colnames(papers),
names(source_track)))),
names = setdiff(colnames(papers), names(source_track))))In some cases, fetching information from (e.g.) the GitHub API fails for a subset of the publications. There are also other reasons for missing values (for example, the earliest submissions do not have an associated pre-review issue). The table below lists the number of missing values for each of the variables in the data frame.
ggplot(papers %>%
dplyr::mutate(pubmonth = lubridate::floor_date(published.date, "month")) %>%
dplyr::group_by(pubmonth) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubmonth), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per month", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))ggplot(papers %>%
dplyr::mutate(pubyear = lubridate::year(published.date)) %>%
dplyr::group_by(pubyear) %>%
dplyr::summarize(npub = n()),
aes(x = factor(pubyear), y = npub)) +
geom_bar(stat = "identity") + theme_minimal() +
labs(x = "", y = "Number of published papers per year", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))The plots below illustrate the fraction of pre-review and review issues closed during each month that have the ‘rejected’ label attached.
ggplot(all_rejected,
aes(x = factor(closedmonth), y = nbr_rejections/nbr_issues_closed)) +
geom_bar(stat = "identity") +
theme_minimal() +
facet_wrap(~ itype, ncol = 1) +
labs(x = "Month of issue closing", y = "Fraction of issues rejected",
caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))Papers with 20 or more citations are grouped in the “>=20” category.
ggplot(papers %>%
dplyr::mutate(citation_count = replace(citation_count,
citation_count >= 20, ">=20")) %>%
dplyr::mutate(citation_count = factor(citation_count,
levels = c(0:20, ">=20"))) %>%
dplyr::group_by(citation_count) %>%
dplyr::tally(),
aes(x = citation_count, y = n)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(x = "Crossref citation count", y = "Number of publications", caption = dcap)The table below sorts the JOSS papers in decreasing order by the number of citations in Crossref.
DT::datatable(
papers %>%
dplyr::mutate(url = paste0("<a href='", url, "' target='_blank'>",
url,"</a>")) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::select(title, url, published.date, citation_count),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)plotly::ggplotly(
ggplot(papers, aes(x = published.date, y = citation_count, label = title)) +
geom_point(alpha = 0.5) + theme_bw() + scale_y_sqrt() +
geom_smooth() +
labs(x = "Date of publication", y = "Crossref citation count", caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)Here, we plot the citation count for all papers published within each half year, sorted in decreasing order.
ggplot(papers %>% dplyr::group_by(halfyear) %>%
dplyr::arrange(desc(citation_count)) %>%
dplyr::mutate(idx = seq_along(citation_count)),
aes(x = idx, y = citation_count)) +
geom_point(alpha = 0.5) +
facet_wrap(~ halfyear, scales = "free") +
theme_bw() +
labs(x = "Index", y = "Crossref citation count", caption = dcap)In these plots we investigate whether the time a submission spends in the pre-review or review stage has changed over time.
ggplot(papers, aes(x = prerev_opened, y = as.numeric(days_in_pre))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of pre-review opening", y = "Number of days in pre-review",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = review_opened, y = as.numeric(days_in_rev))) +
geom_point() + geom_smooth() + theme_bw() +
labs(x = "Date of review opening", y = "Number of days in review",
caption = dcap) +
theme(axis.title = element_text(size = 15))Next, we consider the languages used by the submissions, both as reported by Whedon and based on the information encoded in available GitHub repositories (for the latter, we also record the number of bytes of code written in each language). Note that a given submission can use multiple languages.
## Language information from Whedon
sspl <- strsplit(papers$languages, ",")
all_languages <- unique(unlist(sspl))
langs <- do.call(dplyr::bind_rows, lapply(all_languages, function(l) {
data.frame(language = l,
nbr_submissions_Whedon = sum(vapply(sspl, function(v) l %in% v, 0)))
}))
## Language information from GitHub software repos
a <- lapply(strsplit(papers$repo_languages_bytes, ","), function(w) strsplit(w, ":"))
a <- a[sapply(a, length) > 0]
langbytes <- as.data.frame(t(as.data.frame(a))) %>%
setNames(c("language", "bytes")) %>%
dplyr::mutate(bytes = as.numeric(bytes)) %>%
dplyr::filter(!is.na(language)) %>%
dplyr::group_by(language) %>%
dplyr::summarize(nbr_bytes_GitHub = sum(bytes),
nbr_repos_GitHub = length(bytes)) %>%
dplyr::arrange(desc(nbr_bytes_GitHub))
langs <- dplyr::full_join(langs, langbytes, by = "language")ggplot(langs %>% dplyr::arrange(desc(nbr_submissions_Whedon)) %>%
dplyr::filter(nbr_submissions_Whedon > 10) %>%
dplyr::mutate(language = factor(language, levels = language)),
aes(x = language, y = nbr_submissions_Whedon)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15))DT::datatable(
langs %>% dplyr::arrange(desc(nbr_bytes_GitHub)),
escape = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(langs, aes(x = nbr_repos_GitHub, y = nbr_bytes_GitHub)) +
geom_point() + scale_x_log10() + scale_y_log10() + geom_smooth() +
theme_bw() +
labs(x = "Number of repos using the language",
y = "Total number of bytes of code\nwritten in the language",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplotly(
ggplot(papers, aes(x = citation_count, y = repo_nbr_stars,
label = title)) +
geom_point(alpha = 0.5) + scale_x_sqrt() + scale_y_sqrt() +
theme_bw() +
labs(x = "Crossref citation count", y = "Number of stars, GitHub repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)),
tooltip = c("label", "x", "y")
)ggplot(papers, aes(x = as.numeric(prerev_opened - repo_created))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from repo creation to JOSS pre-review start",
caption = dcap) +
theme(axis.title = element_text(size = 15))ggplot(papers, aes(x = as.numeric(repo_pushed - review_closed))) +
geom_histogram(bins = 50) +
theme_bw() +
labs(x = "Time (days) from closure of JOSS review to most recent commit in repo",
caption = dcap) +
theme(axis.title = element_text(size = 15)) +
facet_wrap(~ year(published.date), scales = "free_y")Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
ggplot(papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)),
aes(x = nbr_reviewers)) + geom_bar() +
facet_wrap(~ year) + theme_bw() +
labs(x = "Number of reviewers", y = "Number of submissions", caption = dcap)Submissions associated with rOpenSci and pyOpenSci are not considered here, since they are not explicitly reviewed at JOSS.
reviewers <- papers %>%
dplyr::filter(!grepl("rOpenSci|pyOpenSci", prerev_labels)) %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::select(reviewers, year) %>%
tidyr::separate_rows(reviewers, sep = ",")
## Most active reviewers
DT::datatable(
reviewers %>% dplyr::group_by(reviewers) %>%
dplyr::summarize(nbr_reviews = length(year),
timespan = paste(unique(c(min(year), max(year))),
collapse = " - ")) %>%
dplyr::arrange(desc(nbr_reviews)),
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE)
)ggplot(papers %>%
dplyr::mutate(year = year(published.date),
`r/pyOpenSci` = factor(
grepl("rOpenSci|pyOpenSci", prerev_labels),
levels = c("TRUE", "FALSE"))),
aes(x = editor)) + geom_bar(aes(fill = `r/pyOpenSci`)) +
theme_bw() + facet_wrap(~ year, ncol = 1) +
scale_fill_manual(values = c(`TRUE` = "grey65", `FALSE` = "grey35")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
labs(x = "Editor", y = "Number of submissions", caption = dcap)all_licenses <- sort(unique(papers$repo_license))
license_levels = c(grep("apache", all_licenses, value = TRUE),
grep("bsd", all_licenses, value = TRUE),
grep("mit", all_licenses, value = TRUE),
grep("gpl", all_licenses, value = TRUE),
grep("mpl", all_licenses, value = TRUE))
license_levels <- c(license_levels, setdiff(all_licenses, license_levels))
ggplot(papers %>%
dplyr::mutate(repo_license = factor(repo_license,
levels = license_levels)),
aes(x = repo_license)) +
geom_bar() +
theme_bw() +
labs(x = "Software license", y = "Number of submissions", caption = dcap) +
theme(axis.title = element_text(size = 15),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
facet_wrap(~ year(published.date), scales = "free_y")## For plots below, replace licenses present in less
## than 2.5% of the submissions by 'other'
tbl <- table(papers$repo_license)
to_replace <- names(tbl[tbl <= 0.025 * nrow(papers)])ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::count() %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = n, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Number of submissions", caption = dcap)ggplot(papers %>%
dplyr::mutate(year = year(published.date)) %>%
dplyr::mutate(repo_license = replace(repo_license,
repo_license %in% to_replace,
"other")) %>%
dplyr::mutate(year = factor(year),
repo_license = factor(
repo_license,
levels = license_levels[license_levels %in% repo_license]
)) %>%
dplyr::group_by(year, repo_license, .drop = FALSE) %>%
dplyr::summarize(n = n()) %>%
dplyr::mutate(freq = n/sum(n)) %>%
dplyr::mutate(year = as.integer(as.character(year))),
aes(x = year, y = freq, fill = repo_license)) + geom_area() +
theme_minimal() +
scale_fill_brewer(palette = "Set1", name = "Software\nlicense",
na.value = "grey") +
theme(axis.title = element_text(size = 15)) +
labs(x = "Year", y = "Fraction of submissions", caption = dcap)a <- unlist(strsplit(papers$repo_topics, ","))
a <- a[!is.na(a)]
topicfreq <- table(a)
colors <- viridis::viridis(100)
set.seed(1234)
wordcloud::wordcloud(
names(topicfreq), sqrt(topicfreq), min.freq = 1, max.words = 300,
random.order = FALSE, rot.per = 0.05, use.r.layout = FALSE,
colors = colors, scale = c(10, 0.1), random.color = TRUE,
ordered.colors = FALSE, vfont = c("serif", "plain")
)Here, we take a more detailed look at the papers that cite JOSS papers, using data from the Open Citations Corpus.
citations <- tryCatch({
citecorp::oc_coci_cites(doi = papers$alternative.id) %>%
dplyr::distinct() %>%
dplyr::mutate(citation_info_obtained = as.character(lubridate::today()))
}, error = function(e) {
NULL
})
dim(citations)## NULL
if (!is.null(citations)) {
citations <- citations %>%
dplyr::filter(!(oci %in% citations_archive$oci))
tmpj <- rcrossref::cr_works(dois = unique(citations$citing))$data %>%
dplyr::select(contains("doi"), contains("container.title"), contains("issn"),
contains("type"), contains("publisher"), contains("prefix"))
citations <- citations %>% dplyr::left_join(tmpj, by = c("citing" = "doi"))
## bioRxiv preprints don't have a 'container.title' or 'issn', but we'll assume
## that they can be
## identified from the prefix 10.1101 - set the container.title
## for these records manually; we may or may not want to count these
## (would it count citations twice, both preprint and publication?)
citations$container.title[citations$prefix == "10.1101"] <- "bioRxiv"
## JOSS is represented by 'The Journal of Open Source Software' as well as
## 'Journal of Open Source Software'
citations$container.title[citations$container.title ==
"Journal of Open Source Software"] <-
"The Journal of Open Source Software"
## Remove real self citations (cited DOI = citing DOI)
citations <- citations %>% dplyr::filter(cited != citing)
## Merge with the archive
citations <- dplyr::bind_rows(citations, citations_archive)
} else {
citations <- citations_archive
if (is.null(citations[["citation_info_obtained"]])) {
citations$citation_info_obtained <- NA_character_
}
}
citations$citation_info_obtained[is.na(citations$citation_info_obtained)] <-
"2021-08-11"
write.table(citations, file = "joss_submission_citations.tsv",
row.names = FALSE, col.names = TRUE, sep = "\t", quote = FALSE)## [1] "2021-12-01"
## Number of JOSS papers with >0 citations included in this collection
length(unique(citations$cited))## [1] 851
## Number of JOSS papers with >0 citations according to Crossref
length(which(papers$citation_count > 0))## [1] 961
## Number of citations from Open Citations Corpus vs Crossref
df0 <- papers %>% dplyr::select(doi, citation_count) %>%
dplyr::full_join(citations %>% dplyr::group_by(cited) %>%
dplyr::tally() %>%
dplyr::mutate(n = replace(n, is.na(n), 0)),
by = c("doi" = "cited"))## [1] 15638
## [1] 12408
## Ratio of total citation count Open Citations Corpus/Crossref
sum(df0$n, na.rm = TRUE)/sum(df0$citation_count, na.rm = TRUE)## [1] 0.7934518
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw()## Zoom in
ggplot(df0, aes(x = citation_count, y = n)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(size = 3, alpha = 0.5) +
labs(x = "Crossref citation count", y = "Open Citations Corpus citation count",
caption = dcap) +
theme_bw() +
coord_cartesian(xlim = c(0, 75), ylim = c(0, 75))## [1] 3166
## [1] 2762
topcit <- citations %>% dplyr::group_by(container.title) %>%
dplyr::summarize(nbr_citations_of_joss_papers = length(cited),
nbr_cited_joss_papers = length(unique(cited)),
nbr_citing_papers = length(unique(citing)),
nbr_selfcitations_of_joss_papers = sum(author_sc == "yes"),
fraction_selfcitations = signif(nbr_selfcitations_of_joss_papers /
nbr_citations_of_joss_papers, digits = 3)) %>%
dplyr::arrange(desc(nbr_cited_joss_papers))
DT::datatable(topcit,
escape = FALSE, rownames = FALSE,
filter = list(position = 'top', clear = FALSE),
options = list(scrollX = TRUE))plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)plotly::ggplotly(
ggplot(topcit, aes(x = nbr_citations_of_joss_papers, y = nbr_cited_joss_papers,
label = container.title)) +
geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "grey") +
geom_point(size = 3, alpha = 0.5) +
theme_bw() +
coord_cartesian(xlim = c(0, 100), ylim = c(0, 50)) +
labs(caption = dcap, x = "Number of citations of JOSS papers",
y = "Number of cited JOSS papers")
)The tibble object with all data collected above is serialized to a file that can be downloaded and reused.
## alternative.id container.title created deposited
## 1 10.21105/joss.00925 Journal of Open Source Software 2018-09-13 2018-09-13
## 2 10.21105/joss.02669 Journal of Open Source Software 2020-10-15 2020-10-15
## 3 10.21105/joss.01720 Journal of Open Source Software 2020-06-21 2020-06-21
## 4 10.21105/joss.01935 Journal of Open Source Software 2020-02-10 2020-02-10
## 5 10.21105/joss.02582 Journal of Open Source Software 2020-10-22 2020-10-22
## 6 10.21105/joss.00505 The Journal of Open Source Software 2018-02-01 2018-02-01
## published.print doi indexed issn issue issued
## 1 2018-09-13 10.21105/joss.00925 2021-12-03 2475-9066 29 2018-09-13
## 2 2020-10-15 10.21105/joss.02669 2021-12-11 2475-9066 54 2020-10-15
## 3 2020-06-21 10.21105/joss.01720 2021-12-11 2475-9066 50 2020-06-21
## 4 2020-02-10 10.21105/joss.01935 2021-12-11 2475-9066 46 2020-02-10
## 5 2020-10-22 10.21105/joss.02582 2021-12-11 2475-9066 54 2020-10-22
## 6 2018-02-01 10.21105/joss.00505 2021-12-11 2475-9066 22 2018-02-01
## member page prefix publisher score source reference.count
## 1 8722 925 10.21105 The Open Journal 0 Crossref 19
## 2 8722 2669 10.21105 The Open Journal 0 Crossref 11
## 3 8722 1720 10.21105 The Open Journal 0 Crossref 4
## 4 8722 1935 10.21105 The Open Journal 0 Crossref 19
## 5 8722 2582 10.21105 The Open Journal 0 Crossref 12
## 6 8722 505 10.21105 The Open Journal 0 Crossref 6
## references.count is.referenced.by.count
## 1 19 28
## 2 11 0
## 3 4 0
## 4 19 4
## 5 12 5
## 6 6 6
## title
## 1 Ripser.py: A Lean Persistent Homology Library for Python
## 2 Psifr: Analysis and visualization of free recall data
## 3 Torsional Axisymmetric Core Oscillations Visualiser (TACO-VIS): A Python module for animating torsional wave data for fluid planetary cores
## 4 GroundwaterDupuitPercolator: A Landlab component for groundwater flow
## 5 21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal.
## 6 Finch: a tool adding dynamic abundance filtering to genomic MinHashing
## type url volume
## 1 journal-article http://dx.doi.org/10.21105/joss.00925 3
## 2 journal-article http://dx.doi.org/10.21105/joss.02669 5
## 3 journal-article http://dx.doi.org/10.21105/joss.01720 5
## 4 journal-article http://dx.doi.org/10.21105/joss.01935 5
## 5 journal-article http://dx.doi.org/10.21105/joss.02582 5
## 6 journal-article http://dx.doi.org/10.21105/joss.00505 3
## short.container.title
## 1 JOSS
## 2 JOSS
## 3 JOSS
## 4 JOSS
## 5 JOSS
## 6 JOSS
## author
## 1 http://orcid.org/0000-0003-4206-1963, http://orcid.org/0000-0002-8549-9810, http://orcid.org/0000-0002-4675-222X, FALSE, FALSE, FALSE, Christopher, Nathaniel, Rann, Tralie, Saul, Bar-On, first, additional, additional
## 2 http://orcid.org/0000-0002-2631-2710, FALSE, Neal, Morton, first
## 3 http://orcid.org/0000-0001-9303-6229, http://orcid.org/0000-0001-7591-6716, NA, FALSE, FALSE, NA, Sam, Philip, Grace, Greenwood, Livermore, Cox, first, additional, additional
## 4 http://orcid.org/0000-0002-8097-4029, http://orcid.org/0000-0003-0364-5800, http://orcid.org/0000-0001-5682-455X, http://orcid.org/0000-0002-3185-002X, FALSE, FALSE, FALSE, FALSE, David, Gregory, Katherine, Ciaran, Litwin, Tucker, Barnhart, Harman, first, additional, additional, additional
## 5 http://orcid.org/0000-0003-3059-3823, http://orcid.org/0000-0002-4085-2094, http://orcid.org/0000-0003-3374-1772, http://orcid.org/0000-0002-8984-0465, http://orcid.org/0000-0002-4314-1810, http://orcid.org/0000-0003-3095-6137, http://orcid.org/0000-0003-1443-3483, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, Steven, Bradley, Andrei, Julian, Yuxiang, Jaehong, Catherine, Murray, Greig, Mesinger, Muñoz, Qin, Park, Watkinson, first, additional, additional, additional, additional, additional, additional
## 6 http://orcid.org/0000-0002-8819-9549, http://orcid.org/0000-0001-8637-406X, FALSE, FALSE, Roderick, Nick, Bovee, Greenfield, first, additional
## citation_count
## 1 28
## 2 0
## 3 0
## 4 4
## 5 5
## 6 6
## api_title
## 1 Ripser.py: A Lean Persistent Homology Library for Python
## 2 Psifr: Analysis and visualization of free recall data
## 3 Torsional Axisymmetric Core Oscillations Visualiser (TACO-VIS): A Python module for animating torsional wave data for fluid planetary cores
## 4 GroundwaterDupuitPercolator: A Landlab component for groundwater flow
## 5 21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal.
## 6 Finch: a tool adding dynamic abundance filtering to genomic MinHashing
## api_state editor reviewers nbr_reviewers
## 1 accepted @arokem @lmcinnes 1
## 2 accepted @cMadan @samhforbes,@paxtonfitzpatrick 2
## 3 accepted @leouieda @malmans2,@banesullivan 2
## 4 accepted @kthyng @dvalters,@nicgaspar 2
## 5 accepted @dfm @sambit-giri,@sultanier 2
## 6 accepted @biorelated @HadrienG 1
## repo_url review_issue_id prereview_issue_id
## 1 https://github.com/scikit-tda/ripser.py 925 901
## 2 https://github.com/mortonne/psifr 2669 2612
## 3 https://github.com/sam-greenwood/taco_vis 1720 1657
## 4 https://github.com/landlab/landlab 1935 1933
## 5 https://github.com/21cmFAST/21cmFAST 2582 2502
## 6 https://github.com/onecodex/finch-rs 505 378
## languages
## 1 Makefile,Python,TeX,C++,C
## 2 Makefile,Python,Matlab,TeX,Shell
## 3 Python
## 4 Python,PowerShell,Batchfile,Shell,Makefile
## 5 Python,Jupyter Notebook,Makefile,Shell,TeX
## 6 TeX,Rust
## archive_doi
## 1 https://doi.org/10.5281/zenodo.1412867
## 2 https://doi.org/10.5281/zenodo.4086188
## 3 https://doi.org/10.5281/zenodo.3902334
## 4 https://doi.org/10.5281/zenodo.3660698
## 5 https://doi.org/10.5281/zenodo.4107189
## 6 http://dx.doi.org/10.5281/zenodo.1164259
## review_title
## 1 Ripser.py: A Lean Persistent Homology Library for Python
## 2 Psifr: Analysis and visualization of free recall data
## 3 Torsional Axisymmetric Core Oscillations Visualiser (TACO-VIS): A python module for animating torsional wave data for fluid planetary cores.
## 4 GroundwaterDupuitPercolator: A Landlab component for groundwater flow
## 5 21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal.
## 6 Finch: MinHashing for Sequencing Data with Abundance Calculation and Adaptive Filtering
## review_number review_state review_opened review_closed review_ncomments
## 1 925 closed 2018-09-02 2018-09-13 25
## 2 2669 closed 2020-09-12 2020-10-15 37
## 3 1720 closed 2019-09-09 2020-06-21 60
## 4 1935 closed 2019-12-04 2020-02-10 59
## 5 2582 closed 2020-08-18 2020-10-22 122
## 6 505 closed 2017-12-13 2018-02-01 27
## review_labels
## 1 accepted,recommend-accept,published
## 2 accepted,Python,Matlab,Makefile,recommend-accept,published
## 3 accepted,recommend-accept,published
## 4 accepted,recommend-accept,published
## 5 accepted,Python,Jupyter Notebook,C,recommend-accept,published
## 6 accepted,recommend-accept,published
## prerev_title
## 1 Ripser.py: A Lean Persistent Homology Library for Python
## 2 Psifr: Analysis and visualization of free recall data
## 3 Torsional Axisymmetric Core Oscillations Visualiser (TACO-VIS): A python module for animating torsional wave data for fluid planetary cores.
## 4 GroundwaterDupuitPercolator: A Landlab component for groundwater flow
## 5 21cmFAST v3: A Python-integrated C code for generating 3D realizations of the cosmic 21cm signal.
## 6 Finch: MinHashing for Sequencing Data with Abundance Calculation and Adaptive Filtering
## prerev_state prerev_opened prerev_closed prerev_ncomments
## 1 closed 2018-08-18 2018-09-02 35
## 2 closed 2020-08-30 2020-09-12 31
## 3 closed 2019-08-17 2019-09-09 31
## 4 closed 2019-12-03 2019-12-04 38
## 5 closed 2020-07-22 2020-08-18 28
## 6 closed 2017-08-25 2017-12-13 23
## prerev_labels days_in_pre days_in_rev to_review repo_created
## 1 Python,Makefile,C++ 15 days 11 days TRUE 2017-07-03
## 2 Python,Matlab,Makefile 13 days 33 days TRUE 2020-03-19
## 3 Python 23 days 286 days TRUE 2019-01-10
## 4 Python,PowerShell,Batchfile 1 days 68 days TRUE 2014-05-09
## 5 Python,Jupyter Notebook,C 27 days 65 days TRUE 2019-06-12
## 6 110 days 50 days TRUE 2016-12-30
## repo_updated repo_pushed repo_nbr_stars repo_language
## 1 2021-11-24 2021-10-27 166 C++
## 2 2021-10-20 2021-10-20 6 Python
## 3 2021-11-26 2021-11-26 1 Python
## 4 2021-12-01 2021-12-01 233 Python
## 5 2021-11-22 2021-11-06 23 C
## 6 2021-12-08 2021-11-29 68 Rust
## repo_languages_bytes
## 1 C++:46217,Python:41024,TeX:16524,Cython:1798,C:487
## 2 Python:87392,MATLAB:6426,TeX:6254,Makefile:641
## 3 Python:50811,TeX:1452
## 4 Python:4259993,Jupyter Notebook:1200986,Cython:219388,TeX:42252,Makefile:1917,Shell:1073
## 5 C:702083,Python:397577,Jupyter Notebook:116373,TeX:9102,Makefile:2104,Shell:389
## 6 Rust:217499,Cap'n Proto:5006,TeX:2566,Dockerfile:809
## repo_topics
## 1 topological-data-analysis,persistent-homology,homology,topology,tda,data-science,ripser
## 2
## 3
## 4
## 5
## 6
## repo_license repo_nbr_contribs repo_nbr_contribs_2ormore repo_info_obtained
## 1 other 16 10 2021-11-29
## 2 gpl-3.0 1 1 2021-12-01
## 3 other 3 2 2021-12-22
## 4 mit 26 21 2021-12-01
## 5 mit 9 8 2021-11-24
## 6 mit 8 5 2021-12-09
## published.date halfyear nbr_authors
## 1 2018-09-13 2018H2 3
## 2 2020-10-15 2020H2 1
## 3 2020-06-21 2020H1 3
## 4 2020-02-10 2020H1 4
## 5 2020-10-22 2020H2 7
## 6 2018-02-01 2018H1 2
To read the current version of this file directly from GitHub, use the following code:
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readr_2.1.1 citecorp_0.3.0 plotly_4.10.0 DT_0.20
## [5] jsonlite_1.7.2 purrr_0.3.4 gh_1.3.0 lubridate_1.8.0
## [9] ggplot2_3.3.5 tidyr_1.1.4 dplyr_1.0.7 rcrossref_1.1.0.99
## [13] tibble_3.1.6
##
## loaded via a namespace (and not attached):
## [1] viridis_0.6.2 httr_1.4.2 sass_0.4.0 splines_4.1.2
## [5] bit64_4.0.5 vroom_1.5.7 viridisLite_0.4.0 bslib_0.3.1
## [9] shiny_1.7.1 highr_0.9 triebeard_0.3.0 urltools_1.7.3
## [13] yaml_2.2.1 lattice_0.20-45 pillar_1.6.4 glue_1.6.0
## [17] digest_0.6.29 RColorBrewer_1.1-2 promises_1.2.0.1 colorspace_2.0-2
## [21] Matrix_1.3-4 htmltools_0.5.2 httpuv_1.6.4 plyr_1.8.6
## [25] pkgconfig_2.0.3 httpcode_0.3.0 xtable_1.8-4 gitcreds_0.1.1
## [29] scales_1.1.1 whisker_0.4 later_1.3.0 tzdb_0.2.0
## [33] mgcv_1.8-38 generics_0.1.1 farver_2.1.0 ellipsis_0.3.2
## [37] withr_2.4.3 lazyeval_0.2.2 cli_3.1.0 magrittr_2.0.1
## [41] crayon_1.4.2 mime_0.12 evaluate_0.14 fansi_0.5.0
## [45] nlme_3.1-153 xml2_1.3.3 tools_4.1.2 data.table_1.14.2
## [49] hms_1.1.1 lifecycle_1.0.1 stringr_1.4.0 munsell_0.5.0
## [53] compiler_4.1.2 jquerylib_0.1.4 rlang_0.4.12 grid_4.1.2
## [57] htmlwidgets_1.5.4 crosstalk_1.2.0 miniUI_0.1.1.1 labeling_0.4.2
## [61] rmarkdown_2.11 gtable_0.3.0 curl_4.3.2 fauxpas_0.5.0
## [65] R6_2.5.1 gridExtra_2.3 knitr_1.37 fastmap_1.1.0
## [69] bit_4.0.4 utf8_1.2.2 stringi_1.7.6 parallel_4.1.2
## [73] crul_1.2.0 Rcpp_1.0.7 vctrs_0.3.8 wordcloud_2.6
## [77] tidyselect_1.1.1 xfun_0.29